Huizing et al.
Figure 4. Adversarial training of an auto-encoder with the GANomaly method to detect spectrograms of unknown targets.
the decimation. This simple and effective preprocessing step is apparently difficult to learn by the CNN from the limited number of training samples in test case C. Another conclusion is that the reduction in coherent integration time in the Doppler filtering from 10.7 ms to 2.7 ms also gives a significant improvement in classification accuracy. This improvement can be explained by the fact that the modulations by the rotating propeller or rotor blades are less smeared in the Doppler spectrum due to the shorter integration time, and the changes in the Doppler spectrum become clearer. Although the use of an LSTM-RNN proved to be less accurate than the CNN in the overall classification, an important advantage of an LSTM-RNN is that it can provide good classification results already after a few coherent processing intervals, whereas the CNN only provides a classification result after an entire spectrogram has been processed [14]. Another advantage of the LSTM-RNN is that it is capable to deal with transitions in target behavior, e.g., a fixed wing mini-UAV that takes off vertically, and then, proceeds to horizontal flight.
DETECTION OF UNKNOWN TARGET CLASSES During military operations, cognitive radars will often be employed in conditions where novel target classes are observed of which there are no examples of micro-Doppler spectrograms in the training set of the deep neural network. In this case, the cognitive radar should detect the presence of this unknown target class and may decide to schedule specific radar measurements to add spectrograms of the unknown target class to the training set. The detection of unknown target classes can be achieved in several ways. The simplest approach is the NOVEMBER 2019
application of a threshold to the output of the Soft-max layer of a CNN [15]. If the maximum output of the Softmax layer does not exceed the threshold, an unknown target class is declared. This Soft-max approach is also referred to as a reject option for a classifier [16]. By varying the value of the threshold, a receiver operation characteristic (ROC) curve can be obtained, which shows the probability of detecting an unknown target class (true positive rate) as a function of the probability of falsely declaring an unknown target class (false positive rate). An alternative to the Soft-max approach called GANomaly is based on an auto-encoder that is trained in an adversarial manner [17]. This technique has been used to investigate the potential of deep learning to screen aviation luggage for anomalous items using X-ray screening. Figure 4 shows the configuration of the GANomaly network that consists of three subnetworks. The auto-encoder acts as a generator network that learns a compact latent space representation z of the input spectrograms x by trying to reconstruct the input spectrogram. The encoder compresses the reconstructed spectrogram to an estimate of the latent representation using the same architecture as the encoder in the auto-encoder. The discriminator determines, with an encoder architecture, if the input is a real spectrogram or a fake spectrogram. The detection of a spectrogram originating from an unknown target assumes that the auto-encoder is not able to reconstruct such a spectrogram accurately because the network is trained only with spectrograms from known target classes. A reconstructed spectrogram for an unknown spectrogram will also lead to discrepancies between the latent vector and its estimate. This discrepancy is used by the GANomaly method during the test phase to detect a spectrogram of an unknown target class.
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